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Saturday 20 January 2018
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10 Tips For Successful Database Management With Analytics

10 Tips For Successful Database Management With Analytics

In the past, data storage units were inert – holding data until you needed to access it. But today, big data analytics tools make it easier than ever to gather business insights from your databases. Here are 10 tips to help you make the most of your databases!

  1. Define Your Needs

Before you even select an analytics platform, you need to define the specific needs of your business. If you choose a tool like Hadoop that doesn’t integrate well with your existing data, you’re only going to waste time and money. 

  1. Focus On The 6 “Vs”

 When choosing a data analytics solution, consider these 6 factors:

  • Velocity (data processing speed)
  • Variety (total number of data types)
  • Volume (amount of data)
  • Vicinity (location)
  • Visibility (global namespace)
  • Veracity (data protection and consistent data availability

Choose a system which has the proper combination of these 6 factors, as needed by your business. 

  1. Build From The Ground-Up

Don’t just add analytics to a file database as an afterthought. Your entire analytics solution should be built from the ground-up as a single, holistic system, and you should also make sure that it can access both modern and legacy data systems. 

  1. Consider Using File Analytics

File analytics can help you analyze and organize unstructured data, allowing you to keep track of important documents and files. These platforms are simple to implement and cost-effective, making them a great investment. 

  1. Turn To The Cloud

Don’t have the storage space or processing power for analytics? Consider an outsourced cloud platform, such as the Amazon S3 cloud computing platform. You can move your data and use a variety of different cloud-based BI tools to gain powerful business insights. 

  1. Manage File Access Wisely

Using multiple data storage systems may require you to purchase annual licenses just to access your data. Keep a close eye on who needs to file access to analyze your data, to ensure you are not wasting license fees. 

  1. Use Advanced Storage Controllers

 Modern storage controllers can process data unbelievably quickly, providing the high performance that’s essential for real-time or near-real-time analytics. 

  1. Implement NVMe Technology

NVMe uses flash-based architecture to deliver data retrieval/writing speeds that are up to 6x faster than competing storage devices, and they will soon replace SCSI as the leading method of data storage. Futureproof by investing in NVMe technology today.

  1. Don’t Silo Your Data

Analytics tools are only useful if you can use them easily across all of your data-sets. So when implementing a new tool or piece of software, beware of data siloing. Ensure that all of your data ends up in the same place, so that it can be easily analyzed. 

  1. Real-Time Isn’t Always Best

Everyone wants real-time data analytics – but not every company needs them. Real-time analytics requires extremely high-end infrastructure, so it can be very expensive indeed.

While industries like banking and e-commerce may need real-time data analytics, most companies can use an object storage solution like Scality RING instead of real-time data analytics solutions like Hadoop.

Make Your Data Analytics Project A Success With These 10 Tips!

With these helpful tips, you’re sure to succeed in your next database analytics project. Take another look, and think about how you can use this advice to your benefit!




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